Keeping first vs last vs none in Pandas - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of drop_duplicates with keep='first'

What is the output DataFrame after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3, 3],
    'B': ['x', 'y', 'y', 'z', 'z', 'z']
})

result = df.drop_duplicates(subset=['A'], keep='first')
print(result)

A

   A  B
1  2  y
3  3  z

B

   A  B
2  2  y
4  3  z

C

   A  B
0  1  x
2  2  y
5  3  z

D

   A  B
0  1  x
1  2  y
3  3  z

💡 Hint

Keep='first' keeps the first occurrence of each duplicate group.

drop_duplicates with keep='first' keeps the first row for each unique value in column 'A'. Rows with A=2 and A=3 appear multiple times, but only the first occurrence is kept.

❓ Predict Output

intermediate

Output of drop_duplicates with keep='last'

What is the output DataFrame after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3, 3],
    'B': ['x', 'y', 'y', 'z', 'z', 'z']
})

result = df.drop_duplicates(subset=['A'], keep='last')
print(result)

A

   A  B
0  1  x
1  2  y
3  3  z

B

   A  B
1  2  y
4  3  z

C

   A  B
0  1  x
2  2  y
5  3  z

D

   A  B
2  2  y
4  3  z

💡 Hint

Keep='last' keeps the last occurrence of each duplicate group.

drop_duplicates with keep='last' keeps the last row for each unique value in column 'A'. For A=2, last occurrence is index 2; for A=3, last occurrence is index 5.

❓ Predict Output

advanced

Output of drop_duplicates with keep=False

What is the output DataFrame after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3, 3],
    'B': ['x', 'y', 'y', 'z', 'z', 'z']
})

result = df.drop_duplicates(subset=['A'], keep=False)
print(result)

A

   A  B
1  2  y
3  3  z

B

   A  B
0  1  x

C Empty DataFrame\nColumns: [A, B]\nIndex: []

D

   A  B
0  1  x
1  2  y
3  3  z

💡 Hint

Keep=False drops all duplicates, keeping only unique rows.

drop_duplicates with keep=False removes all rows that have duplicates in column 'A'. Only A=1 is unique, so only that row remains.

🔧 Debug

advanced

Identify the error in drop_duplicates usage

What error does this code raise when executed?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2],
    'B': ['x', 'y', 'y']
})

result = df.drop_duplicates(subset=['A'], keep='middle')
print(result)

A ValueError: keep must be either 'first', 'last', or False

B TypeError: unhashable type: 'list'

C KeyError: 'middle'

D No error, outputs DataFrame with all rows

💡 Hint

Check the allowed values for the keep parameter.

The keep parameter only accepts 'first', 'last', or False. Using 'middle' raises a ValueError.

🚀 Application

expert

Count unique rows after drop_duplicates with different keep values

Given this DataFrame, how many rows remain after applying drop_duplicates on column 'A' with keep='first', keep='last', and keep=False respectively?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3, 3, 4],
    'B': ['x', 'y', 'y', 'z', 'z', 'z', 'w']
})

count_first = len(df.drop_duplicates(subset=['A'], keep='first'))
count_last = len(df.drop_duplicates(subset=['A'], keep='last'))
count_none = len(df.drop_duplicates(subset=['A'], keep=False))

print(count_first, count_last, count_none)

A 4 4 2

B 4 4 1

C 5 5 3

D 5 5 2

💡 Hint

Count unique values and consider which rows are kept or removed.

There are 4 unique values in 'A': 1, 2, 3, 4. keep='first' and keep='last' keep one row per unique value, so 4 rows remain. keep=False removes all duplicates, so only unique values that appear once remain: 1 and 4, so 2 rows remain.

Practice

1.

In pandas, what does keep='first' do when removing duplicates?

easy

A. Keeps the first occurrence of each duplicate and removes the rest

B. Keeps the last occurrence of each duplicate and removes the rest

C. Removes all duplicates, keeping only unique rows

D. Keeps all duplicates without removing any

Solution

1. Step 1: Understand the meaning of keep='first'

   This option keeps the first row of each set of duplicates and removes the later ones.

2. Step 2: Compare with other options

   Unlike keep='last' which keeps the last occurrence, keep='first' keeps the earliest one.

3. Final Answer:

   Keeps the first occurrence of each duplicate and removes the rest -> Option A

4. Quick Check:

   keep='first' = Keep first duplicate [OK]

Hint: Remember: 'first' keeps earliest duplicate row [OK]

Common Mistakes:

• Confusing keep='first' with keep='last'
• Thinking keep='first' removes all duplicates
• Assuming keep='first' keeps all duplicates

2.

Which of the following is the correct syntax to remove duplicates in a pandas DataFrame df keeping only the last occurrence?

df._____('column_name', keep='last')

easy

A. drop_dupes

B. remove_duplicates

C. drop_duplicates

D. delete_duplicates

Solution

1. Step 1: Identify the pandas method for removing duplicates

   The correct method is drop_duplicates to remove duplicate rows.

2. Step 2: Confirm syntax for keeping last occurrence

   Using keep='last' inside drop_duplicates keeps the last duplicate row.

3. Final Answer:

   drop_duplicates -> Option C

4. Quick Check:

   Method to remove duplicates = drop_duplicates [OK]

Hint: Use df.drop_duplicates() to remove duplicates [OK]

Common Mistakes:

• Using non-existent methods like remove_duplicates
• Confusing method names with similar words
• Forgetting to specify keep='last'

3.

What will be the output of the following code?

import pandas as pd

data = {'A': [1, 2, 2, 3, 3, 3], 'B': ['x', 'y', 'y', 'z', 'z', 'z']}
df = pd.DataFrame(data)
result = df.drop_duplicates(keep=False)
print(result)

medium

A. Rows with unique values only, no duplicates

B. All rows except the first occurrence of duplicates

C. Rows with A=1 B='x', A=2 B='y', A=3 B='z' only

D. Empty DataFrame

Solution

1. Step 1: Understand keep=False behavior

   Using keep=False removes all duplicates, keeping only rows that appear once.

2. Step 2: Identify unique rows in the DataFrame

   In the data, only the row with A=1 and B='x' is unique; others appear multiple times.

3. Final Answer:

   Rows with unique values only, no duplicates -> Option A

4. Quick Check:

   keep=False = Keep only unique rows [OK]

Hint: keep=False drops all duplicates, keeps only unique rows [OK]

Common Mistakes:

• Thinking keep=False keeps first or last duplicates
• Expecting some duplicates to remain
• Confusing output with keep='first' or keep='last'

4.

Consider this code snippet:

import pandas as pd

data = {'col': [1, 1, 2, 2, 3]}
df = pd.DataFrame(data)
result = df.drop_duplicates(keep='middle')
print(result)

What is the error in this code?

medium

A. DataFrame missing required columns

B. Invalid value for the 'keep' parameter

C. Syntax error in DataFrame creation

D. No error, code runs fine

Solution

1. Step 1: Check the 'keep' parameter values

   The valid values for 'keep' are 'first', 'last', or False. 'middle' is invalid.

2. Step 2: Identify the error raised by invalid 'keep'

   Using 'middle' causes a ValueError because pandas does not recognize it.

3. Final Answer:

   Invalid value for the 'keep' parameter -> Option B

4. Quick Check:

   keep must be 'first', 'last', or False [OK]

Hint: Use only 'first', 'last', or False for keep parameter [OK]

Common Mistakes:

• Using unsupported strings for keep
• Assuming 'middle' is valid
• Ignoring error messages

5.

You have a DataFrame df with duplicate rows based on column 'ID'. You want to remove all duplicates completely, so only rows with unique 'ID' remain. Which code snippet achieves this?

1) df.drop_duplicates(subset=['ID'], keep='first')
2) df.drop_duplicates(subset=['ID'], keep='last')
3) df.drop_duplicates(subset=['ID'], keep=False)
4) df.drop_duplicates()

hard

A. Option 1

B. Option 4

C. Option 2

D. Option 3

Solution

1. Step 1: Understand the goal

   The goal is to remove all duplicates so only unique 'ID' rows remain.

2. Step 2: Analyze each option

   Options 1 and 2 keep one duplicate (first or last). Option 4 removes duplicates but keeps first by default. Only option 3 with keep=False removes all duplicates completely.

3. Final Answer:

   Option 3 -> Option D

4. Quick Check:

   keep=False removes all duplicates [OK]

Hint: Use keep=False to remove all duplicates fully [OK]

Common Mistakes:

• Using keep='first' or 'last' when full removal needed
• Not specifying subset for duplicate check
• Assuming default drop_duplicates removes all duplicates